17 citations · 41 across the 14 of their papers we have counts for
14 papers
Agentic Adversarial Rewriting Exposes Architectural Vulnerabilities in Black-Box NLP Pipelines
Mazal Bethany, Kim-Kwang Raymond Choo, Nishant Vishwamitra +1
Multi-component natural language processing (NLP) pipelines are increasingly deployed for high-stakes decisions, yet no existing adversarial method can test their robustness under…
CAMOUFLAGE: Exploiting Misinformation Detection Systems Through LLM-driven Adversarial Claim Transformation
Mazal Bethany, Nishant Vishwamitra, Cho-Yu Jason Chiang +1
Automated evidence-based misinformation detection systems, which evaluate the veracity of short claims against evidence, lack comprehensive analysis of their adversarial vulnerabil…
Enhancing Event Reasoning in Large Language Models through Instruction Fine-Tuning with Semantic Causal Graphs
Mazal Bethany, Emet Bethany, Brandon Wherry +4
Event detection and text reasoning have become critical applications across various domains. While LLMs have recently demonstrated impressive progress in reasoning abilities, they…
Can Reinforcement Learning Unlock the Hidden Dangers in Aligned Large Language Models?
Mohammad Bahrami Karkevandi, Nishant Vishwamitra, Peyman Najafirad
Large Language Models (LLMs) have demonstrated impressive capabilities in natural language tasks, but their safety and morality remain contentious due to their training on internet…
RASTeR: Robust, Agentic, and Structured Temporal Reasoning
Dan Schumacher, Fatemeh Haji, Tara Grey +7
Temporal question answering (TQA) remains a challenge for large language models (LLMs), particularly when retrieved content may be irrelevant, outdated, or temporally inconsistent.…
AI-Cybersecurity Education Through Designing AI-based Cyberharassment Detection Lab
Ebuka Okpala, Nishant Vishwamitra, Keyan Guo +7
Cyberharassment is a critical, socially relevant cybersecurity problem because of the adverse effects it can have on targeted groups or individuals. While progress has been made in…
Moderating Illicit Online Image Promotion for Unsafe User-Generated Content Games Using Large Vision-Language Models
Keyan Guo, Ayush Utkarsh, Wenbo Ding +5
Online user generated content games (UGCGs) are increasingly popular among children and adolescents for social interaction and more creative online entertainment. However, they pos…
Image Safeguarding: Reasoning with Conditional Vision Language Model and Obfuscating Unsafe Content Counterfactually
Mazal Bethany, Brandon Wherry, Nishant Vishwamitra +1
Social media platforms are being increasingly used by malicious actors to share unsafe content, such as images depicting sexual activity, cyberbullying, and self-harm. Consequently…
An Investigation of Large Language Models for Real-World Hate Speech Detection
Keyan Guo, Alexander Hu, Jaden Mu +4
Hate speech has emerged as a major problem plaguing our social spaces today. While there have been significant efforts to address this problem, existing methods are still significa…
Lateral Phishing With Large Language Models: A Large Organization Comparative Study
Mazal Bethany, Athanasios Galiopoulos, Emet Bethany +4
The emergence of Large Language Models (LLMs) has heightened the threat of phishing emails by enabling the generation of highly targeted, personalized, and automated attacks. Tradi…
Deciphering Textual Authenticity: A Generalized Strategy through the Lens of Large Language Semantics for Detecting Human vs. Machine-Generated Text
Mazal Bethany, Brandon Wherry, Emet Bethany +3
With the recent proliferation of Large Language Models (LLMs), there has been an increasing demand for tools to detect machine-generated text. The effective detection of machine-ge…
Moderating New Waves of Online Hate with Chain-of-Thought Reasoning in Large Language Models
Nishant Vishwamitra, Keyan Guo, Farhan Tajwar Romit +4
Online hate is an escalating problem that negatively impacts the lives of Internet users, and is also subject to rapid changes due to evolving events, resulting in new waves of onl…
Understanding and Measuring Robustness of Multimodal Learning
Nishant Vishwamitra, Hongxin Hu, Ziming Zhao +2
The modern digital world is increasingly becoming multimodal. Although multimodal learning has recently revolutionized the state-of-the-art performance in multimodal tasks, relativ…
CrescendoNet: A Simple Deep Convolutional Neural Network with Ensemble Behavior
Xiang Zhang, Nishant Vishwamitra, Hongxin Hu +1
We introduce a new deep convolutional neural network, CrescendoNet, by stacking simple building blocks without residual connections. Each Crescendo block contains independent convo…